from baselines.common.atari_wrappers import make_atari, wrap_deepmind
from dqn_agent import Agent
pong_game = "PongNoFrameskip-v4"
pong_reward_threshold = 19.0
pong_model_filepath = "pong_model.json"
pong_model_weights_filepath = "pong_model.h5"
env = make_atari(pong_game)
env = wrap_deepmind(env, frame_stack=True, scale=True)
env.seed(seed)
pong_agent = Agent(env, reward_threshold=pong_reward_threshold,
model_filepath=pong_model_filepath,
model_weights_filepath=pong_model_weights_filepath)
pong_agent.train()
pong_agent.run()
from baselines.common.atari_wrappers import make_atari, wrap_deepmind
from dqn_agent import Agent
seed = 42
env = make_atari("BreakoutNoFrameskip-v4")
# Warp the frames, grey scale, stake four frame and scale to smaller ratio
env = wrap_deepmind(env, frame_stack=True, scale=True)
env.seed(seed)
breakout_agent = Agent(env)
breakout_agent.train()
breakout_agent.run()
def main(limit_episode, limit_step, seed=0, load=False):
rospy.init_node('dqn_pendulum_myrrbot7')
pub = rospy.Publisher("/myrrbot7/joint1_cotroller/command",
Float64,
queue_size=1)
loop_rate = rospy.Rate(hz)
result_path = "/home/amsl/ros_catkin_ws/src/deep_actor_critic/actor_critic_for_swingup/test_results/results/dqn_result.txt"
model_path = "/home/amsl/ros_catkin_ws/src/deep_actor_critic/actor_critic_for_swingup/test_results/models/dqn_"
init_theta = 0.0
init_omega = 0.0
state = get_state(init_theta, init_omega)
state_dash = get_state(init_theta, init_omega)
action = 0.0
reward = 0.0
action_list = [
np.array([a], dtype=np.float64) for a in [min_torque, max_torque]
]
n_st = len(state[0])
n_act = len(action_list)
Q_list = np.array([])
max_Q = 0.0
ave_Q = 0.0
reward_list = np.array([])
ave_reward = 0.0
total_reward = 0.0
temp_result = np.array([[]])
test_result = np.array([[]])
evaluation_flag = False
wait_flag = True
agent = Agent(n_st, n_act, seed)
if load:
agent.load_model(model_path)
episode_count = 0
time = 1
count = 0
wait_count = 0
while not rospy.is_shutdown():
if wait_flag:
wait_count += 1
# print "wait_count : ", wait_count
state = get_state(init_theta, init_omega)
state_dash = get_state(init_theta, init_omega)
reset_client(init_theta)
action = 0.0
pub.publish(action)
if wait_count == 0.5 * hz:
init_theta = uniform(-1 * math.pi, math.pi)
if wait_count % hz == 0:
wait_count = 0
wait_flag = False
# print "Please Wait 1 second"
# print "state : ", state
# print "state_dash : ", state_dash
else:
if not evaluation_flag:
# print "Now Learning!!!!!"
# print "episode : ", episode_count
# print "time : ", time
# print "state : ", state
act_i, q = agent.get_action(state, False)
Q_list = np.append(Q_list, [q])
# print "act_i : ", act_i
action = action_list[act_i]
# print "action : ", action
pub.publish(action)
theta, omega = get_joint_properties('myrrbot7_joint1')
# print "theta : %f, omega : %f" % (theta, omega)
state_dash = get_state(theta, omega)
# print "state_dash : ", state_dash
reward = get_reward(theta, omega, action)
reward_list = np.append(reward_list, [reward])
# print "reward : ", reward
ep_end = get_ep_end(theta, time, limit_step)
# print "ep_end : ", ep_end
agent.stock_experience(count, state, act_i, reward, state_dash,
ep_end)
agent.train(count)
time += 1
count += 1
if ep_end:
max_Q = np.max(Q_list)
ave_Q = np.average(Q_list)
# print "max_Q : ", max_Q
# print "ave_Q : ",ave_Q
ave_reward = np.average(reward_list)
total_reward = np.sum(reward_list)
# print "ave_reward : ", ave_reward
# print "total_reward : ", total_reward
print "Episode : %d\t/Reward Sum : %f\tEpsilon : %f\tLoss : %f\t/Average Q : %f\t/Time Step : %d" % (
episode_count, total_reward, agent.epsilon, agent.loss,
np.sum(Q_list) / float(time), agent.step + 1)
Q_list = np.array([])
reward_list = np.array([])
temp_result = np.array(([[
episode_count, max_Q, ave_Q, ave_reward, total_reward
]]),
dtype=np.float32)
if episode_count == 0:
# print "test_result : ", test_result
test_result = temp_result
# print "test_result : ", test_result
else:
test_result = np.r_[test_result, temp_result]
save_result(result_path, test_result)
agent.save_model(model_path)
if episode_count % 1 == 0:
evaluation_flag = False
episode_count += 1
time = 0
wait_flag = True
else:
# print "Now evaluation!!!"
# print "episode : ", episode_count-1
# print "time : ", time
# print "state : ", state
act_i, q = agent.get_action(state, True)
Q_list = np.append(Q_list, [q])
# print "act_i : ", act_i
# print "Q_list : ", Q_list
action = action_list[act_i]
# print "action : ", action
pub.publish(action)
theta, omega = get_joint_properties('myrrbot7_joint1')
# print "theta : %f, omega : %f" % (theta, omega)
state_dash = get_state(theta, omega)
# print "state_dash : ", state_dash
reward = get_reward(theta, omega, action)
# print "reward : ", reward
reward_list = np.append(reward_list, [reward])
# print "reward_list : ", reward_list
ep_end = get_ep_end(theta, time, limit_step)
# print "ep_end : ", ep_end
if ep_end:
max_Q = np.max(Q_list)
ave_Q = np.average(Q_list)
# print "max_Q : ", max_Q
# print "ave_Q : ",ave_Q
ave_reward = np.average(reward_list)
total_reward = np.sum(reward_list)
# print "ave_reward : ", ave_reward
# print "total_reward : ", total_reward
print "Episode : %d\t/Reward Sum : %f\tEpsilon : %f\tLoss : %f\t/Average Q : %f\t/Time Step : %d" % (
episode_count - 1, total_reward, agent.epsilon,
agent.loss, np.sum(Q_list) / float(time + 1),
agent.step)
Q_list = np.array([])
reward_list = np.array([])
time = 0
wait_flag = True
evaluation_flag = False
temp_result = np.array(([[
episode_count - 1, max_Q, ave_Q, ave_reward,
total_reward
]]),
dtype=np.float32)
if episode_count - 1 == 0:
# print "test_result : ", test_result
test_result = temp_result
# print "test_result : ", test_result
else:
test_result = np.r_[test_result, temp_result]
save_result(result_path, test_result)
agent.save_model(model_path)
time += 1
loop_rate.sleep()
with open(os.path.join(save_dir, "path.txt"), "a") as myfile:
myfile.write('iteration {}, rl_cost {}: {}\n'.format(
global_step, tsp_cost, dqn_env.steps))
completed_episodes += 1
episode_reward.append(ep_reward)
episode_length.append(t)
rl_cost.append(tsp_computer.rl_cost(dqn_env.steps))
# No changes to either Q and target Q network until the replay memory has been filled
# with <replay_start_size> random transitions
if not c.test and number_of_observations_experienced >= replay_start_size:
# Train the Q network once every <update_frequency> iterations
if number_of_observations_experienced % update_frequency == 0:
agent.train()
# Update target Q network weights once every <target_network_update_frequency> iterations to be equal
# to the Q network weights
if number_of_observations_experienced % target_network_update_frequency == 0:
agent.update_target_Q_network_weights()
# Decrease exploration probability
agent.update_exploration_value()
# New timestep
t += 1
global_step += 1
number_of_observations_experienced += 1
o_t = o_tp1
if c.test:
